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Background and aimOnly 10% of pancreatic ductal adenocarcinoma (PDAC) patients survive longer than five years. Factors underlining long-term survivorship in PDAC are not well understood. Therefore, we aimed to identify the key players in the tumor immune microenvironment (TIME) associated with long-term survivorship in PDAC patients.MethodsThe immune-related gene expression profiles of resected PDAC tumors of patients who survived and remained recurrence-free of disease for ≥36 months (long-term survivors, n=10) were compared to patients who had survived ≤6 months (short-term survivors, n=10) due to tumor recurrence. Validation was performed by the spatial protein expression profile of immune cells using the GeoMx™ Digital Spatial Profiler. An independent cohort of samples consisting of 12 long-term survivors and 10 short-term survivors, was used for additional validation. The independent validation was performed by combining qualitative immunohistochemistry and quantitative protein expression profiling.ResultsB cells were found to be significantly increased in the TIME of long-term survivors by gene expression profiling (p=0.018). The high tumor infiltration of B cells was confirmed by spatial protein profiling in the discovery and the validation cohorts (p=0.002 and p=0.01, respectively). The higher number of infiltrated B cells was found mainly in the stromal compartments of PDAC samples and was exclusively found within tumor cells in long-term survivors.ConclusionThis is the first comprehensive study that connects the immune landscape of gene expression profiles and protein spatial infiltration with the survivorship of PDAC patients. We found a higher number and a specific location of B cells in TIME of long-term survivors which emphasizes the importance of B cells and B cell-based therapy for future personalized immunotherapy in PDAC patients.

Common variable immunodeficiency (CVID), characterized by recurrent infections, low serum class-switched immunoglobulin isotypes, and poor antigen-specific antibody responses, comprises a heterogeneous patient population in terms of clinical presentation and underlying etiology. The diagnosis is regularly associated with a severe decrease of germinal center (GC)-derived B-cell populations in peripheral blood. However, data from B-cell differentiation within GC is limited. We present a multiplex approach combining histology, flow cytometry, and B-cell receptor repertoire analysis of sorted GC B-cell populations allowing the modeling of distinct disturbances in GCs of three CVID patients. Our results reflect pathophysiological heterogeneity underlying the reduced circulating pool of post-GC memory B cells and plasmablasts in the three patients. In patient 1, quantitative and qualitative B-cell development in GCs is relatively normal. In patient 2, irregularly shaped GCs are associated with reduced somatic hypermutation (SHM), antigen selection, and class-switching, while in patient 3, high SHM, impaired antigen selection, and class-switching with large single clones imply increased re-cycling of cells within the irregularly shaped GCs. In the lymph nodes of patients 2 and 3, only limited numbers of memory B cells and plasma cells are formed. While reduced numbers of circulating post GC B cells are a general phenomenon in CVID, the integrated approach exemplified distinct defects during GC maturation ranging from near normal morphology and function to severe disturbances with different facets of impaired maturation of memory B cells and/or plasma cells. Integrated dissection of disturbed GC B-cell maturation by histology, flow cytometry, and BCR repertoire analysis contributes to unraveling defects in the essential steps during memory formation.

IntroductionA decentralized and multi-platform-compatible molecular diagnostic tool for kidney transplant biopsies could improve the dissemination and exploitation of this technology, increasing its clinical impact. As a first step towards this molecular diagnostic tool, we developed and validated a classifier using the genes of the Banff-Human Organ Transplant (B-HOT) panel extracted from a historical Molecular Microscope® Diagnostic system microarray dataset. Furthermore, we evaluated the discriminative power of the B-HOT panel in a clinical scenario.Materials and MethodsGene expression data from 1,181 kidney transplant biopsies were used as training data for three random forest models to predict kidney transplant biopsy Banff categories, including non-rejection (NR), antibody-mediated rejection (ABMR), and T-cell-mediated rejection (TCMR). Performance was evaluated using nested cross-validation. The three models used different sets of input features: the first model (B-HOT Model) was trained on only the genes included in the B-HOT panel, the second model (Feature Selection Model) was based on sequential forward feature selection from all available genes, and the third model (B-HOT+ Model) was based on the combination of the two models, i.e. B-HOT panel genes plus highly predictive genes from the sequential forward feature selection. After performance assessment on cross-validation, the best-performing model was validated on an external independent dataset based on a different microarray version.ResultsThe best performances were achieved by the B-HOT+ Model, a multilabel random forest model trained on B-HOT panel genes with the addition of the 6 most predictive genes of the Feature Selection Model (ST7, KLRC4-KLRK1, TRBC1, TRBV6-5, TRBV19, and ZFX), with a mean accuracy of 92.1% during cross-validation. On the validation set, the same model achieved Area Under the ROC Curve (AUC) of 0.965 and 0.982 for NR and ABMR respectively.DiscussionThis kidney transplant biopsy classifier is one step closer to the development of a decentralized kidney transplant biopsy classifier that is effective on data derived from different gene expression platforms. The B-HOT panel proved to be a reliable highly-predictive panel for kidney transplant rejection classification. Furthermore, we propose to include the aforementioned 6 genes in the B-HOT panel for further optimization of this commercially available panel.

The immune response affects tumor biological behavior and progression. The specific immune characteristics of pancreatic ductal adenocarcinoma (PDAC) can determine the metastatic abilities of cancerous cells and the survival of patients. Therefore, it is important to characterize the specific immune landscape in PDAC tissue samples, and the effect of various types of therapy on that immune composition. Previously, a set of marker genes was identified to assess the immune cell composition in different types of cancer tissue samples. However, gene expression and subtypes of immune cells may vary across different types of cancers. The aim of this study was to provide a method to identify immune cells specifically in PDAC tissue samples. The method is based on defining a specific set of marker genes expressed by various immune cells in PDAC samples. A total of 90 marker genes were selected and tested for immune cell type-specific definition in PDAC; including 43 previously used, and 47 newly selected marker genes. The immune cell-type specificity was checked mathematically by calculating the “pairwise similarity” for all candidate genes using the PDAC RNA-sequenced dataset available at The Cancer Genome Atlas. A set of 55 marker genes that identify 22 different immune cell types for PDAC was created. To validate the method and the set of marker genes, an independent mRNA expression dataset of 24 samples of PDAC patients who received various types of (neo)adjuvant treatments was used. The results showed that by applying our method we were able to identify PDAC specific marker genes to characterize immune cell infiltration in tissue samples. The method we described enabled identifying different subtypes of immune cells that were affected by various types of therapy in PDAC patients. In addition, our method can be easily adapted and applied to identify the specific immune landscape in various types of tissue samples.

Common variable immune deficiency disorder (CVID) is the most clinically relevant cause of antibody failure. It is a highly heterogeneous disease with different underlying etiologies. CVID has been associated with a quantitative B cell defect, however, little is known about the quality of B cells present. Here, we studied the naïve and antigen selected B-cell receptor (BCR) repertoire in 33 CVID patients using next generation sequencing, to investigate B cells quality. Analysis for each individual patient revealed whether they have a defect in immune repertoire formation [V(D)J recombination] or specification (somatic hypermutation, subclass distribution, or selection). The naïve BCR repertoire was normal in most of the patients, although alterations in repertoire diversity and the junctions were found in a limited number of patients indicating possible defects in early B-cell development or V(D)J recombination in these patients. In contrast, major differences were found in the antigen selected BCR repertoire. Here, most patients (15/17) showed a reduced frequency of somatic hypermutation (SHM), changes in subclass distribution and/or minor alterations in antigen selection. Together these data show that in our CVID cohort only a small number of patients have a defect in formation of the naïve BCR repertoire, whereas the clear majority of patients have disturbances in their antigen selected repertoire, suggesting a defect in repertoire specification in the germinal centers of these patients. This highlights that CVID patients not only have a quantitative B cell defect, but that also the quality of, especially post germinal center B cells, is impaired.

Upon antigen recognition via their B cell receptor (BR) B cells migrate to the germinal center where they undergo somatic hypermutation (SHM) to increase their affinity for the antigen, and class switch recombination (CSR) to change the effector function of the secreted antibodies. These steps are essential to create an antigen-experienced BR repertoire that efficiently protects the body against pathogens. At the same time, the BR repertoire should be selected to protect against responses to self-antigen or harmless antigens. Insights into the processes of SHM, selection and CSR can be obtained by studying the antigen experienced BR repertoire. Currently, a large reference data set of healthy children and adults, which ranges from neonates to the elderly, is not available. In this study, we analyzed the antigen-experienced repertoire of 38 healthy donors (HD), ranging from cord blood to 74 years old, by sequencing IGA and IGG transcripts using next generation sequencing. This resulted in a large, freely available reference data set containing 412,890 IGA and IGG transcripts. We used this data set to study mutation levels, SHM patterns, antigenic selection and CSR from birth to elderly HD. Only small differences were observed in SHM patterns, while the mutation levels increase in early childhood, and stabilize at six years of age at around 7%. Furthermore, comparison of the antigen-experienced repertoire with sequences from the naïve immune repertoire showed that features associated with auto-immunity such as long CDR3 length and IGHV4-34 usage are reduced in the antigen experienced repertoire. Moreover, IGA2 and IGG2 usage was increased in HD in higher age categories, while IGG1 usage was decreased. In addition, we studied clonal relationship in the different samples. Clonally related sequences were found with different subclasses. Interestingly, we found transcripts with the same CDR1-CDR3 sequence, but different subclasses. Together these data suggests that a single antigen can provoke a B-cell response with BR of different subclasses, and that during the course of an immune response some B cells change their isotype without acquiring additional SHM, or can directly switch to different isotypes.

The antigen receptor repertoires of B and T cells form the basis of the adaptive immune response. The repertoires should be sufficiently diverse to recognize all possible pathogens. However, careful selection is needed to prevent responses to self or harmless antigens. Limited antigen receptor repertoire diversity leads to immunodeficiency, whereas unselected or misdirected repertoires can result in autoimmunity. The antigen receptor repertoire harbors information about abnormalities in many immunological disorders. Recent developments in next generation sequencing allow the analysis of the antigen receptor repertoire in much greater detail than ever before. Analyzing the antigen receptor repertoire in patients with mutations in genes responsible for the generation of the antigen receptor repertoire will give new insights into repertoire formation and selection. In this perspective we describe strategies and considerations for analysis of the naive and antigen selected B-cell repertoires in primary immunodeficiency (PID) patients with a focus on severe combined immunodeficiency (SCID) and common variable immunodeficiency (CVID).